Projecting future distributions of ecosystem climate niches in British Columbia

With accumulating evidence showing the ecological impacts of global climate change, scientists, land managers, and policymakers in British Columbia have become increasingly concerned about its impact on local ecosystems. One of the major concerns is the mismatch between the climate that an ecosystem is adapted to and the climate that the ecosystem will experience in the future. If such a mismatch occurs, the health and productivity of the ecosystem are likely to be compromised. Niche-based bioclimate envelope models have been widely used to project future geographic distributions of ecosystem climate niches. However, challenges arising from model accuracy as well as the uncertainties of future climates make it difficult to apply the model projections with confidence in developing adaptive strategies in natural resource management. The bioclimate envelope models are built based on the relationships between the observed presence of an ecosystem type and the climatic conditions of a given ecosystem. However, such relationships are complicated and difficult to model. For the future climates, there are over 140 climate change scenarios from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report, and they vary substantially in magnitude as well as in spatial and temporal patterns. Using different climate change scenarios may lead to totally different adaptation strategies. Several individual scenarios can be averaged to create an “ensemble scenario,” but specific details are lost in this process. The Centre for Forest Conservation Genetics, Department of Forest Sciences, University of British Columbia (Tongli Wang and Sally Aitken), in collaboration with the Ministry of Forests, Lands, and Natural Resources Operations (Elizabeth Campbell and Greg O’Neill), have accurately modelled the Biogeoclimatic (BGC) ecosystem zones with climate variables. The model was built using Random Forest (a machine-learning classifier) with high-resolution climate variables generated by ClimateWNA (http://www.genetics.forestry.ubc.ca/cfcg/climate-models.html) and validated with an independent dataset. Consensus projections based on multiple climate change scenarios were used to cope with the uncertainty in future climate. The major results of this study are summarized below.